README.md

---
base_model:
- Tongyi-MAI/Z-Image
base_model_relation: finetune
frameworks: PyTorch
language:
- en
- zh
library_name: diffusers
license: apache-2.0
pipeline_tag: text-to-image
tasks:
- text-to-image-synthesis
tags:
  - Z
---
## Z-Image-Distilled V2 2026/2/05

To a certain extent, the problem of ZIB color deviation has been reduced, but it is recommended to adjust the color appropriately according to the art style

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    <img src="6.png" width="1200"/>
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- inference cfg: 1.0（建议1.0）
- inference steps: 10（10-15步）
- sampler / scheduler: Euler / simple

The art style leans towards realism
Retains ZIB's creative ability and reduces the collapse of Human anatomy.

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    <img src="3.png" width="1200"/>
</p>

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    <img src="4.png" width="1200"/>
</p>

## Z-Image-Distilled V1 2026/1/30

This model is a **direct distillation-accelerated version** based on the original **Z-Image** (non-Turbo) source. Its purpose is to test LoRA training effects on the Z-Image (non-turbo) version while significantly improving inference/test speed. The model **does not incorporate any weights or style from Z-Image-Turbo** at all — it is a **pure-blood version** based purely on Z-Image, effectively retaining the original Z-Image's adaptability, random diversity in outputs, and overall image style.

Compared to the official Z-Image, inference is much faster (good results achievable in just 10–20 steps); compared to the official Z-Image-Turbo, this model preserves stronger diversity, better LoRA compatibility, and greater fine-tuning potential, though it is slightly slower than Turbo (still far faster than the original Z-Image's 28–50 steps).

The model is mainly suitable for:
- Users who want to train/test LoRAs on the Z-Image non-Turbo base
- Scenarios needing faster generation than the original without sacrificing too much diversity and stylistic freedom
- Artistic, illustration, concept design, and other generation tasks that require a certain level of randomness and style variety
- Compatible with ComfyUI inference (layer prefix == model.diffusion_model)

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    <img src="0.png" width="1200"/>
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### Usage Instructions:

Basic workflow: please refer to the Z-Image-Turbo official workflow (fully compatible with the official Z-Image-Turbo workflow)

Recommended inference parameters:
- inference **cfg**: 1.0–2.5 (recommended range: 1.0~1.8; higher values enhance prompt adherence)
- inference **steps**: 10–20 (10 steps for quick previews, 15–20 steps for more stable quality)
- sampler / scheduler: **Euler / simple**, or **res_m**, or any other compatible sampler

LoRA compatibility is good; recommended weight: 0.6~1.0, adjust as needed.

Also on: [Civitai](https://civitai.com/models/958009/redcraft-or-redzimage-or-updated-jan30-or-latest-redzib-dx1) | [Modelscope AIGC](https://modelscope.cn/models/AiMETATRON/Z-Image-Distilled)
#### RedCraft | 红潮造相 ⚡️ REDZimage | Updated-JAN30 | Latest - RedZiB ⚡️ DX1 Distilled Acceleration

### Current Limitations & Future Directions

**Current main limitations:**
- The distillation process causes some damage to **text (especially very small-sized text)**, with rendering clarity and completeness inferior to the original Z-Image
- Overall color tone remains consistent with the original ZI, but **certain samplers** can produce color cast issues (particularly noticeable excessive blue tint)

**Next optimization directions:**
- Further stabilize generation quality under **CFG=1** within **10 steps or fewer**, striving to achieve more usable results that are closer to the original style even at very low step counts
- Optimize negative prompt adherence when **CFG > 1**, improving control over negative descriptions and reducing interference from unwanted elements
- Continue improving clarity and readability in small text areas while maintaining the speed advantages brought by distillation

We welcome feedback and generated examples from all users — let's collaborate to advance this pure-blood acceleration direction!

### Model License:

Please follow the **Apache-2.0** license of the Z-Image model.

Please follow the **Apache-2.0** open source license for the Z-Image model.